class_docstr: A joint intent-slot model. This is framed as a model to do document
  classification model and word tagging tasks where the embedding and text
  representation layers are shared for both tasks.
  
  The supported representation layers are based on bidirectional LSTM or CNN.
  
  It can be instantiated just like any other :class:`~Model`.
  
  This is in the new data handling design involving tensorizers; that is the
  difference between this and JointModel
code: def __init__(
      self,
      default_doc_loss_weight,
      default_word_loss_weight,
      *args,
      **kwargs,
  ):
      super().__init__(*args, **kwargs)
      # CRF module has parameters and it's forward function is not called in
      # model's forward function because of ONNX compatibility issue. This will
      # not work with DDP, thus setting find_unused_parameters to False to work
      # around, can be removed once DDP support params not used in model forward
      # function
      self.find_unused_parameters = False
      self.default_doc_loss_weight = default_doc_loss_weight
      self.default_word_loss_weight = default_word_loss_weight
      log_class_usage(__class__)
  
imports: from typing import Optional, Union
  import pytext.utils.cuda
  import torch
  from pytext.common.constants import SpecialTokens
  from pytext.data.tensorizers import FloatTensorizer, LabelTensorizer, SlotLabelTensorizer, TokenTensorizer
  from pytext.exporters.exporter import ModelExporter
  from pytext.models.embeddings import WordEmbedding
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from pytext.models.representations.pass_through import PassThroughRepresentation
  from pytext.utils.usage import log_class_usage
  from .decoders import IntentSlotModelDecoder
  from .embeddings import EmbeddingList
  from .output_layers.intent_slot_output_layer import IntentSlotOutputLayer
  from .representations.bilstm_doc_slot_attention import BiLSTMDocSlotAttention
  from .representations.jointcnn_rep import JointCNNRepresentation, SharedCNNRepresentation
label_desc: Use image node_id 1 to create a new IntentSlotModel object from inherited base classes: Model with example: obj = IntentSlotModel(default_doc_loss_weight, default_word_loss_weight)